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Record W2034860447 · doi:10.1007/s12594-012-0030-2

Geothermal Modeling for the Base of Gas Hydrate Stability Zone and Saturation of Gas Hydrate in the Krishna-Godavari Basin, Eastern Indian Margin

2012· article· en· W2034860447 on OpenAlexaff
Uma Shankar, Kalachand Sain, Michael Riedel

Bibliographic record

VenueJournal of the Geological Society of India · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologyClathrate hydrateGeothermal gradientSeafloor spreadingSaturation (graph theory)TectonicsSedimentStructural basinPetrologyMineralogyHydrateGeomorphologySeismologyGeophysics

Abstract

fetched live from OpenAlex

Abstract The passive eastern Indian margin is rich in gas hydrates, as inferred from the wide-spread occurrences of bottom-simulating reflectors (BSRs) and recovery of gas hydrate samples from various sites in the Krishna Godavari (KG) and Mahanadi (MN) basins drilled by the Expedition 01 of the Indian National Gas Hydrate Program (NGHP). The BSRs are often interpreted to mark the thermally controlled base of gas hydrate stability zone (BGHSZ). Most of the BSRs exhibit moderate to typically higher amplitudes than those from other seismic reflectors. We estimate the average geothermal gradient of ∼40°C/km and heat flow varying from 23 to 62 mW/m2 in the study area utilizing the BSR’s observed on seismic sections. Further we provide the BGHSZ where the BSR is not continuous or disturbed by local tectonics or hidden by sedimentation patterns parallel to the seafloor with a view to understand the nature of BSR. Since, gas hydrate bearing sediment has higher electrical resistivities than that of the host sediment, we estimate two levels of gas hydrates saturations up to 25% in the depth interval between 70 to 82, and less than 20% in the depth interval between 90 to 104 meter below the seafloor using the resistivity log data at site 15 of NGHP-01.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.225
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2012
Admission routes1
Has abstractyes

Explore more

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